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Articles

Geographic Profiling and Event Prediction for Seventeenth- and Eighteenth-Century Pirates

Pages 781-791 | Received 17 Jul 2021, Accepted 22 Feb 2022, Published online: 29 Jun 2022
 

Abstract

Modern crime analysts have multiple tools at their disposal, including geographic profiling software and crime prediction software. This study uses said software to analyze data from descriptions of pirate attacks in the seventeenth and eighteenth centuries in the Caribbean. The results show significant similarities between historical pirates and modern criminals, but also point to important differences. Although both crime prediction and geographic profiling algorithms show a degree of accuracy, many pirates often changed their anchor point, making it difficult to find appropriate crime series. When searching for artifacts related to pirate activity, criminological tools might be helpful, but only if used carefully.

犯罪分析人员有很多工具, 包括地理分析软件和犯罪预测软件。本研究使用这些软件, 分析了17世纪和18世纪加勒比海盗袭击的描述数据。结果表明, 历史上的海盗与现代罪犯有显著的相似性, 本文也指出其重要差异。尽管犯罪预测方法和地理分析方法都比较准确, 但是由于许多海盗经常变换位置, 因此很难找到合适的犯罪序列数据。在寻找海盗活动文物时, 只有在小心使用的情况下犯罪学工具才可能有所帮助。

Los analistas modernos de criminología tienen a su disposición múltiples herramientas, entre las cuales se incluyen programas informáticos para elaborar perfiles geográficos y predictivos de acciones criminales. El presente estudio usa dicho software para analizar los datos de narrativas sobre ataques de piratas en el Caribe, durante los siglos XVII y XVIII. Los resultados muestran similitudes significativas entre los piratas históricos y los delincuentes modernos, aunque también destacan diferencias importantes. Si bien tanto los algoritmos de predicción de acciones criminales como los de elaboración de perfiles geográficos muestran cierto grado de precisión, a menudo muchos piratas cambiaban su punto de anclaje, haciendo difícil encontrar las series apropiadas de criminalidad. A la hora de buscar artefactos relacionados con la piratería, las herramientas criminológicas podrían ser útiles, pero sólo si se usan con cuidado.

Acknowledgment

The author would like to thank Nathan Drake as the inspiration for this article.

Additional information

Notes on contributors

Michał Górski

MICHAŁ GÓRSKI has finished PhD studies at the College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences (MISMaP) and defended his dissertation at the Faculty of Law and Administration, University of Warsaw, Warszawa, 02-097 Poland. E-mail: [email protected]. His research interests include crime analysis, geographic profiling, and implementation of machine learning in judicial context.

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